Responsible Data Use: Ethics and Privacy Protection

Careless data analysis that ignores privacy or fairness can cause serious harm to individuals and organizations.

For example, in 2019, Google paid a $170 million fine to the U.S. Federal Trade Commission (FTC) for collecting data from children on YouTube without proper consent.

Practicing ethical and responsible data use is a vital skill for every data analyst.

When analyzing data, always review the following key points:

• Privacy: Are personally identifiable details safely protected and not exposed?
• Consent: Was proper consent obtained when collecting the data?
• Bias: Is the dataset skewed or underrepresenting certain groups?
• Security: Is the data stored and managed securely?

Sensitive details such as names, emails, or ages should be collected lawfully and anonymized before any analysis or sharing.

When handling sensitive data, analysts often apply anonymization — the process of removing or masking personally identifiable information so individuals cannot be traced.

Here's a simple Python example demonstrating how to anonymize names in personal data:

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# Example data containing names and ages
data = [
    {"name": "Lina", "age": 25},
    {"name": "Marcus", "age": 30}
]

# Replace names with a generic placeholder to protect privacy
for person in data:
    person["name"] = "REDACTED"  # Anonymize the name

# Print anonymized data
print(data)

• The dataset includes names and ages collected from a survey.
• To protect privacy, each name is replaced with REDACTED.
• This simple step helps safeguard personal information before sharing or analysis.